Cells make decisions all the time as part of their normal life cycles. These decisions range from the expression of metabolic preferences to choices critical to human health such as whether cells will enter a state of unchecked proliferation or express antibiotic resistance genes. The overarching goal of the work proposed here is to decipher the rules that manage cellular decisions and to subject models of the regulatory process to stringent experimental tests with the aim of developing a predictive understanding of the regulatory code. Though the kinds of models we develop can be applied to both prokaryotic and eukaryotic regulatory circuits, we are focusing first on bacteria in order to build up an entire suite of tools taking us from regulatory architecture discovery at basepair resolution all the way to the systematic quantitative determination of the input-output functions for these circuits. E. coli is one of the best understood of organisms. And yet, out of its more than four thousand genes, we know almost nothing about how half of them are regulated. The work proposed here is built around three main aims that together will result in a predictive picture of transcriptional regulation. The work in the first aim uses a method known as Sort-Seq that makes it possible to identify the constellation of binding sites for the transcription factors that control a given gene of interest. We will use this method as a tool both to design synthetic networks and as the basis of discovering the regulatory architectures for a number of important bacterial genes for which essentially nothing is known. With this regulatory information in hand, in the second aim, we will characterize the input-output response of regulatory elements whose architectures have been characterized using the Sort-Seq approach. Specifically, systematic experiments will be performed to test how variability in gene expression depends upon key parameters, such as the number of transcription factors and the strength of transcription factor binding sites. One of the most mysterious classes of regulatory network involves the binding of transcription factors at a distance from the genes they control. The third aim develops a mechanistic understanding of how these trans-acting factors control the promoter of interest. This analysis will be made using a combination of in vivo experiments and single-molecule measurements. The outcome of this work will be a robust framework permitting us to go all the way from regulatory architecture discovery with base pair resolution to the systematic quantitative determination of the input- output functions of generic regulatory architectures.